When video is taken through turbulent air, as may be the case when an aircraft-mounted video camera obtains video of the ground, image degradation (e.g., blurring) may be produced by atmospheric turbulence in the air through which light travels on its way from the scene to the video camera. This blurring may degrade the quality of the video and diminish its value to an operator observing the video, or to processing equipment using the video for automated analysis or control.
For Electro-Optical (EO) and Infrared (IR) imaging, performance of the conventional imaging systems is often limited by atmospheric seeing conditions. In these systems, imagery from an optical system experiences different spatial motion (e.g., rotation at edges, moving targets, etc.) throughout the scene, but some temporal filters may be able to perform optimally on features being co-spatially located. Nevertheless, non-congruent digital manipulation of individual subset images create “blocky” larger mosaic video and applying a non-linear filter causes a non-unity gain that needs to be corrected in each tile of the mosaic. Low frequency conditions are also challenging to correct and require different spatial compensation and stitching to avoid image artifacts.
Additionally, real-world scenes with high-definition and high-dynamic-range sensors need robust processes. Moreover, handling an image with subsets images can cause non-uniformities between the tiles and thus extra care is required to match gain and level between the subsets images. Also, image optimization processes that use global frame registration require precise results, across the full Field of View (FOV).
Thus, there is a need for a system and method for mitigating the effects of turbulence, for example, atmospheric turbulence on video data, especially across a full Field of View.